- Base pública de dados vetoriais / raster (rnaturalearth)
- Trazer mapas de fundo de serviços online (ggmap)
- Mapas dinâmicos (leaflet, mapview)
8/26/2019
https://www.naturalearthdata.com/
Base de dados de domínio público, nas escalas 1:10m, 1:50m, and 1:110 milhões. Contém dados no formato vetorial e raster para uso em cartografia ou GIS.
Lista de dados disponíveis https://www.naturalearthdata.com/features/
library(rnaturalearth) library(rnaturalearthdata) ufs <- ne_states(country = 'Brazil', returnclass = 'sf') ggplot(ufs) + geom_sf()
world <- ne_countries(scale = "medium", returnclass = "sf")
ggplot(data = world) +
geom_sf(aes(fill = pop_est)) +
scale_fill_viridis_c(option = "plasma", trans = "sqrt")
Obtem mapa de servidores online
library(ggmap)
## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.
## Please cite ggmap if you use it! See citation("ggmap") for details.
perfis <- readRDS('data/perfis.RDS')
br <- c(left = -75, bottom = -35, right = -33, top = 6)
brasil <- get_map(br, source = "stamen")
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ggmap(brasil) + geom_point(data = perfis, aes(x = lon, y = lat)) + labs(title = "Soil profiles in Hybras database")
## Warning: Removed 4 rows containing missing values (geom_point).
sul <- c(left = -60, bottom = -35, right = -33, top = -20) ##br_sul <- get_map(sul, maptype = "toner-lite", source = 'stamen', force = TRUE) ## bug - trazendo mapa errado br_sul <- get_stamenmap(sul, maptype = "toner-lite", zoom = 6, force = TRUE)
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ggmap(br_sul) + geom_point(data = perfis, aes(x = lon, y = lat, col = BR_Soil_Taxonomy)) + labs(title = "Soil profiles in Hybras database")
## Warning: Removed 116 rows containing missing values (geom_point).
cps_bb <- c(left = -47.1, bottom = -22.9, right = -47, top = -22.8) cps <- get_stamenmap(cps_bb, zoom = 14, maptype = "toner-lite")
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ggmap(cps) + geom_point(data = perfis, aes(x = lon, y = lat, col = BR_Soil_Taxonomy)) + labs(title = "Soil profiles in Hybras database")
## Warning: Removed 515 rows containing missing values (geom_point).
require(leaflet)
## Loading required package: leaflet
m <- leaflet()
m <- addTiles(m)
m <- setView(m, -50, -20, zoom=4)
m <- addMarkers(m, lng=-47, lat=-23, popup="Campinas") # Oncoto?
# adicionando do geoserver do IBGE
m <- addWMSTiles(m, 'https://geoservicos.ibge.gov.br/geoserver/ows',
layers='CCAR:BC250_Terra_Indigena_A',
options = WMSTileOptions(format = "image/png",
transparent=TRUE),
attribution = 'teste')
library(mapview)
library(leafem)
koppen_orig <- read_stars('data/koppen_zarc_resample.tif')
koppen <- st_warp(koppen_orig, crs = st_crs(3857), method = "ngb")
#koppen <- projectRasterForLeaflet(koppen_orig, method = "ngb")
cores <- c("#7bae65", "#314999", "#221f65", "#aac31b", "#549a48", "#b8c016",
"#e8e607", "#45922a", "#d39525", "#8ec0e1", "#275c26", "#3a7bc7")
legenda <- c("Cwa", "Am", "Af", "Cfa", "Cwb", "Csb", "Csa", "Cfb",
"BSh", "As", "Cwc", "Aw")
pal <- colorFactor(cores, 1:12, na.color = 'transparent')
Warnings e mensagens suprimidas
m <- leaflet() %>%
addTiles(group = 'OSM') %>%
# addRasterImage(koppen, group = 'koppen', project = FALSE, colors = pal) %>%
addStarsImage(koppen, group = 'koppen', project = FALSE, colors = pal) %>%
leafem::addFeatures(ufs, color = 'black', weight = 2, opacity = 1,
fill = FALSE, group = 'estado' ) %>%
addMarkers(lng = perfis$lon, lat = perfis$lat, popup = perfis$BR_Soil_Taxonomy) %>%
addLayersControl(
overlayGroups = c('koppen', 'estado')) %>%
addLegend("bottomleft",
colors = cores,
labels = legenda,
opacity = 1
)
Warnings e mensagens suprimidas
É possível enviar os mapas criados para servidores online, como o RPubs.
Cuidado ao incluir Rasters. Pode deixar o tamanho do arquivo bem grande
## R version 3.4.4 (2018-03-15) ## Platform: x86_64-pc-linux-gnu (64-bit) ## Running under: Ubuntu 16.04.6 LTS ## ## Matrix products: default ## BLAS: /usr/lib/libblas/libblas.so.3.6.0 ## LAPACK: /usr/lib/lapack/liblapack.so.3.6.0 ## ## attached base packages: ## [1] stats graphics grDevices utils datasets methods base ## ## other attached packages: ## [1] leafem_0.0.1 mapview_2.7.0 ## [3] leaflet_2.0.2 ggmap_3.0.0 ## [5] rnaturalearthdata_0.1.0 rnaturalearth_0.1.0 ## [7] stars_0.3-2 abind_1.4-5 ## [9] sf_0.7-7 forcats_0.4.0 ## [11] stringr_1.4.0 dplyr_0.8.3 ## [13] purrr_0.3.2 readr_1.3.1 ## [15] tidyr_0.8.3 tibble_2.1.3 ## [17] ggplot2_3.2.0 tidyverse_1.2.1 ## ## loaded via a namespace (and not attached): ## [1] nlme_3.1-137 bitops_1.0-6 ## [3] satellite_1.0.1 lubridate_1.7.4 ## [5] webshot_0.5.1 httr_1.4.0 ## [7] tools_3.4.4 backports_1.1.4 ## [9] R6_2.4.0 KernSmooth_2.23-15 ## [11] rgeos_0.4-3 DBI_1.0.0 ## [13] lazyeval_0.2.2 colorspace_1.4-1 ## [15] raster_2.9-23 withr_2.1.2 ## [17] sp_1.3-1 tidyselect_0.2.5 ## [19] curl_4.0 compiler_3.4.4 ## [21] cli_1.1.0 rvest_0.3.4 ## [23] xml2_1.2.1 labeling_0.3 ## [25] scales_1.0.0 classInt_0.3-3 ## [27] digest_0.6.20 rmarkdown_1.14 ## [29] base64enc_0.1-3 jpeg_0.1-8 ## [31] pkgconfig_2.0.2 htmltools_0.3.6 ## [33] htmlwidgets_1.3 rlang_0.4.0 ## [35] readxl_1.3.1 rstudioapi_0.10 ## [37] shiny_1.3.2 generics_0.0.2 ## [39] jsonlite_1.6 crosstalk_1.0.0 ## [41] magrittr_1.5 Rcpp_1.0.2 ## [43] munsell_0.5.0 stringi_1.4.3 ## [45] yaml_2.2.0 plyr_1.8.4 ## [47] grid_3.4.4 parallel_3.4.4 ## [49] promises_1.0.1 crayon_1.3.4 ## [51] lattice_0.20-38 haven_2.1.1 ## [53] hms_0.5.0 zeallot_0.1.0 ## [55] knitr_1.23 pillar_1.4.2 ## [57] rjson_0.2.20 codetools_0.2-15 ## [59] stats4_3.4.4 glue_1.3.1 ## [61] evaluate_0.14 modelr_0.1.4 ## [63] png_0.1-7 vctrs_0.2.0 ## [65] httpuv_1.5.1 RgoogleMaps_1.4.3 ## [67] cellranger_1.1.0 gtable_0.3.0 ## [69] assertthat_0.2.1 xfun_0.8 ## [71] mime_0.7 lwgeom_0.1-7 ## [73] xtable_1.8-4 broom_0.5.2 ## [75] e1071_1.7-2 later_0.8.0 ## [77] rnaturalearthhires_0.2.0 class_7.3-14 ## [79] viridisLite_0.3.0 units_0.6-3